III:Small: Commugrate -- A Community-based Data Integration System

该项目的目标是构建Commugrate，一个社区驱动的数据集成系统，利用人类社区与数据源的交互所获得的信息。Commugrate解决了科学，工程和工业中使用的信息源数量增加所带来的关键挑战，以及对大规模数据集成解决方案的需求，以实现对这些资源的有效访问。这些挑战包括模式匹配和映射，记录链接和数据修复。更具体地说，Commugrate（i）利用来自不同类型的人类社区的直接和间接贡献，重点是后者的贡献，（ii）使用新的证据解决关键的数据集成问题，如以前没有使用过的使用和行为数据，（iii）采用新的模式匹配技术，其定义了其自身的新类别，即基于使用的模式匹配，（iv）引入其基于实体行为的记录链接的第一种类型技术，以及（v）提供自适应反馈系统以通过最佳地利用用户反馈来提高数据的质量。Commugrate对社会的多个领域产生了广泛的影响，因为数据集成是迄今为止科学，工程和工业许多领域中最重要，同时也是最棘手的问题。此外，利用用户与数据源的交互，特别是间接交互，可以提供若干益处，并帮助解决许多棘手的数据集成任务，这些任务在没有人为干预的情况下无法完成。博士生将在这个项目中进行研究。本研究的出版物、技术报告、软件和实验数据将通过项目网站http://www.purdue.edu/cybercenter/commugrate传播。

The goal of this project is to build Commugrate, a community-driven data integration system that capitalizes on the information gainedfrom the interactions of communities of humans with data sources.Commugrate tackles key challenges raised by the increase in thenumber of information sources used in science, engineering, andindustry, as well as the need for large-scale data integrationsolutions to enable effective access to these sources. These challengesinclude schema matching and mapping, record-linkage, and data repair.More specifically, Commugrate (i) utilizes both direct and indirectcontributions from different types of human communities with a focus onthe latter contributions, (ii) solves key data integration issues using new evidences like usage and behavior data which have not been previously used, (iii) adopts a new technique for schema matching, which defines a new class of its own, namely usage-based schema matching, (iv) introduces the first of its genre technique for record linkage based on entities' behavior, and (v) provides an adaptive feedback system to improve the quality of the data by making the best use of users feedback. Commugrate has a broad impact across multiple segments of society as data integration is by far the most important and in the same time vexing issue in many areas in sciences, engineering, and industry. Furthermore, leveraging users' interactions with data sources, especially indirect interaction, may provide several benefits and help solve many intractable data integration tasks which cannot be done without human intervention. PhD students will pursue research in this project. Publications, technical reports, software and experimental data from this research will be disseminated via the project web site at http://www.purdue.edu/cybercenter/commugrate.